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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 401–425 of 4002 papers

TitleStatusHype
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level AttacksCode0
Projective Methods for Mitigating Gender Bias in Pre-trained Language ModelsCode0
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili—0
Advancing Fake News Detection: Hybrid DeepLearning with FastText and Explainable AI—0
A comparative analysis of embedding models for patent similarity—0
An efficient domain-independent approach for supervised keyphrase extraction and ranking—0
Empowering Segmentation Ability to Multi-modal Large Language ModelsCode0
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction—0
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech RepresentationsCode0
Identifying and interpreting non-aligned human conceptual representations using language modeling—0
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis—0
The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical NotesCode0
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources—0
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic ChangeCode0
Ontology Enhanced Claim Detection—0
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings—0
Word Embeddings Revisited: Do LLMs Offer Something New?—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Semi-Supervised Learning for Bilingual Lexicon InductionCode0
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts—0
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random FeaturesCode0
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition—0
Predicting ATP binding sites in protein sequences using Deep Learning and Natural Language Processing—0
Graph-based Clustering for Detecting Semantic Change Across Time and LanguagesCode0
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding AlteringCode0
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